Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in the United States in Near Real-Time

Circulating levels of both seasonal and pandemic influenza require constant surveillance to ensure the health and safety of the population. While up-to-date information is critical, traditional surveillance systems can have data availability lags of up to two weeks. We introduce a novel method of estimating, in near-real time, the level of influenza-like illness (ILI) in the United States (US) by monitoring the rate of particular Wikipedia article views on a daily basis. We calculated the number of times certain influenza- or health-related Wikipedia articles were accessed each day between December 2007 and August 2013 and compared these data to official ILI activity levels provided by the Centers for Disease Control and Prevention (CDC). We developed a Poisson model that accurately estimates the level of ILI activity in the American population, up to two weeks ahead of the CDC, with an absolute average difference between the two estimates of just 0.27% over 294 weeks of data. Wikipedia-derived ILI models performed well through both abnormally high media coverage events (such as during the 2009 H1N1 pandemic) as well as unusually severe influenza seasons (such as the 2012–2013 influenza season). Wikipedia usage accurately estimated the week of peak ILI activity 17% more often than Google Flu Trends data and was often more accurate in its measure of ILI intensity. With further study, this method could potentially be implemented for continuous monitoring of ILI activity in the US and to provide support for traditional influenza surveillance tools

Probing Brownstein-Moffat Gravity via Numerical Simulations

In the standard scenario of the Newtonian gravity, a late-type galaxy (i.e., a spiral galaxy) is well described by a disk and a bulge embedded in a halo mainly composed by dark matter. In Brownstein-Moffat gravity, there is a claim that late-type galaxy systems would not need to have halos, avoiding as a result the dark matter problem, i.e., a modified gravity (non-Newtonian) would account for the galactic structure with no need of dark matter. In the present paper, we probe this claim via numerical simulations. Instead of using a "static galaxy," where the centrifugal equilibrium is usually adopted, we probe the Brownstein-Moffat gravity dynamically via numerical $N$-body simulations.Comment: 33 pages and 14 figures - To appear in The Astrophysical Journa

Using search queries for malaria surveillance, Thailand

Background: Internet search query trends have been shown to correlate with incidence trends for select infectious diseases and countries. Herein, the first use of Google search queries for malaria surveillance is investigated. The research focuses on Thailand where real-time malaria surveillance is crucial as malaria is re-emerging and developing resistance to pharmaceuticals in the region. Methods: Official Thai malaria case data was acquired from the World Health Organization (WHO) from 2005 to 2009. Using Google correlate, an openly available online tool, and by surveying Thai physicians, search queries potentially related to malaria prevalence were identified. Four linear regression models were built from different sub-sets of malaria-related queries to be used in future predictions. The models’ accuracies were evaluated by their ability to predict the malaria outbreak in 2009, their correlation with the entire available malaria case data, and by Akaike information criterion (AIC). Results: Each model captured the bulk of the variability in officially reported malaria incidence. Correlation in the validation set ranged from 0.75 to 0.92 and AIC values ranged from 808 to 586 for the models. While models using malaria-related and general health terms were successful, one model using only microscopy-related terms obtained equally high correlations to malaria case data trends. The model built strictly of queries provided by Thai physicians was the only one that consistently captured the well-documented second seasonal malaria peak in Thailand. Conclusions: Models built from Google search queries were able to adequately estimate malaria activity trends in Thailand, from 2005–2010, according to official malaria case counts reported by WHO. While presenting their own limitations, these search queries may be valid real-time indicators of malaria incidence in the population, as correlations were on par with those of related studies for other infectious diseases. Additionally, this methodology provides a cost-effective description of malaria prevalence that can act as a complement to traditional public health surveillance. This and future studies will continue to identify ways to leverage web-based data to improve public health

Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance

We present a machine learning-based methodology capable of providing real-time ("nowcast") and forecast estimates of influenza activity in the US by leveraging data from multiple data sources including: Google searches, Twitter microblogs, nearly real-time hospital visit records, and data from a participatory surveillance system. Our main contribution consists of combining multiple influenza-like illnesses (ILI) activity estimates, generated independently with each data source, into a single prediction of ILI utilizing machine learning ensemble approaches. Our methodology exploits the information in each data source and produces accurate weekly ILI predictions for up to four weeks ahead of the release of CDC's ILI reports. We evaluate the predictive ability of our ensemble approach during the 2013-2014 (retrospective) and 2014-2015 (live) flu seasons for each of the four weekly time horizons. Our ensemble approach demonstrates several advantages: (1) our ensemble method's predictions outperform every prediction using each data source independently, (2) our methodology can produce predictions one week ahead of GFT's real-time estimates with comparable accuracy, and (3) our two and three week forecast estimates have comparable accuracy to real-time predictions using an autoregressive model. Moreover, our results show that considerable insight is gained from incorporating disparate data streams, in the form of social media and crowd sourced data, into influenza predictions in all time horizon

Big brother is watching - using digital disease surveillance tools for near real-time forecasting

Abstract for the International Journal of Infectious Diseases 79 (S1) (2019).https://www.ijidonline.com/article/S1201-9712(18)34659-9/abstractPublished versio

Police Ethics in Rural Contexts: A Left Realist Consequentialist View

This article presents crime survey data from the state of West Virginia in the United States showing that, controlling for structural conditions, community atmosphere is significantly related to crime, violence and many other social problems in rural places. These results help identify measurable and achievable progressive desired ends in rural policing, replacing law enforcement outputs (for example, arrests, gun and drug seizures) with safe, strong community outcomes as the summum bonum (i.e, ultimate outcome) of policing. Findings show that interdependent communities where police are partners with residents are the safest, while conflict communities where the police are viewed as adversaries are least safe. These results suggest a left realist consequentialist approach to police ethics to dismantle the hegemony of draconian policies and practices.

Enhanced hippocampal long-term potentiation and spatial learning in aged 11ß-hydroxysteroid dehydrogenase type 1 knock-out mice

Glucocorticoids are pivotal in the maintenance of memory and cognitive functions as well as other essential physiological processes including energy metabolism, stress responses, and cell proliferation. Normal aging in both rodents and humans is often characterized by elevated glucocorticoid levels that correlate with hippocampus-dependent memory impairments. 11ß-Hydroxysteroid dehydrogenase type 1 (11ß-HSD1) amplifies local intracellular ("intracrine") glucocorticoid action; in the brain it is highly expressed in the hippocampus. We investigated whether the impact of 11ß-HSD1 deficiency in knock-out mice (congenic on C57BL/6J strain) on cognitive function with aging reflects direct CNS or indirect effects of altered peripheral insulin-glucose metabolism. Spatial learning and memory was enhanced in 12 month "middle-aged" and 24 month "aged" 11ß-HSD1<sup>–/–</sup> mice compared with age-matched congenic controls. These effects were not caused by alterations in other cognitive (working memory in a spontaneous alternation task) or affective domains (anxiety-related behaviors), to changes in plasma corticosterone or glucose levels, or to altered age-related pathologies in 11ß-HSD1<sup>–/–</sup> mice. Young 11ß-HSD1<sup>–/–</sup> mice showed significantly increased newborn cell proliferation in the dentate gyrus, but this was not maintained into aging. Long-term potentiation was significantly enhanced in subfield CA1 of hippocampal slices from aged 11ß-HSD1<sup>–/–</sup> mice. These data suggest that 11ß-HSD1 deficiency enhances synaptic potentiation in the aged hippocampus and this may underlie the better maintenance of learning and memory with aging, which occurs in the absence of increased neurogenesis

Safety and tolerability of SRX246, a vasopressin 1a antagonist, in irritable Huntington\u27s disease patients—A randomized phase 2 clinical trial

SRX246 is a vasopressin (AVP) 1a receptor antagonist that crosses the blood-brain barrier. It reduced impulsive aggression, fear, depression and anxiety in animal models, blocked the actions of intranasal AVP on aggression/fear circuits in an experimental medicine fMRI study and demonstrated excellent safety in Phase 1 multiple-ascending dose clinical trials. The present study was a 3-arm, multicenter, randomized, placebo-controlled, double-blind, 12-week, dose escalation study of SRX246 in early symptomatic Huntington\u27s disease (HD) patients with irritability. Our goal was to determine whether SRX246 was safe and well tolerated in these HD patients given its potential use for the treatment of problematic neuropsychiatric symptoms. Participants were randomized to receive placebo or to escalate to 120 mg twice daily or 160 mg twice daily doses of SRX246. Assessments included standard safety tests, the Unified Huntington\u27s Disease Rating Scale (UHDRS), and exploratory measures of problem behaviors. The groups had comparable demographics, features of HD and baseline irritability. Eighty-two out of 106 subjects randomized completed the trial on their assigned dose of drug. One-sided exact-method confidence interval tests were used to reject the null hypothesis of inferior tolerability or safety for each dose group vs. placebo. Apathy and suicidality were not affected by SRX246. Most adverse events in the active arms were considered unlikely to be related to SRX246. The compound was safe and well tolerated in HD patients and can be moved forward as a candidate to treat irritability and aggression

A Minimal Length from the Cutoff Modes in Asymptotically Safe Quantum Gravity

Within asymptotically safe Quantum Einstein Gravity (QEG), the quantum 4-sphere is discussed as a specific example of a fractal spacetime manifold. The relation between the infrared cutoff built into the effective average action and the corresponding coarse graining scale is investigated. Analyzing the properties of the pertinent cutoff modes, the possibility that QEG generates a minimal length scale dynamically is explored. While there exists no minimal proper length, the QEG sphere appears to be "fuzzy" in the sense that there is a minimal angular separation below which two points cannot be resolved by the cutoff modes.Comment: 26 pages, 1 figur